Scalable Analytics in Python w/ Dask
What is Dask?
Dask provides high-level Array, Bag, and DataFrame collections that mimic NumPy, lists, and Pandas but can operate in parallel on datasets that don’t fit into main memory. Low Level schedulers: Dask provides dynamic task schedulers that execute task graphs in parallel.
Dask vs Spark
Dask is an Alternative to Spark.
- Spark dataframes will be much better when you have large SQL-style queries (think 100+ line queries) where their query optimizer can kick in.
- Dask dataframes will be much better when queries go beyond typical database queries. This happens most often in time series, random access, and other complex computations.
- Spark will integrate better with JVM and data engineering technology. Spark will also come with everything pre-packaged. Spark is its own ecosystem.
- Dask will integrate better with Python code. Dask is designed to integrate with other libraries and pre-existing systems. If you’re coming from an existing Pandas-based workflow then it’s usually much easier to evolve to Dask.
High level performance of Pandas, Dask, Spark, and Arrow
How does Dask dataframe performance compare to Pandas? Also, what about Spark dataframes and what about Arrow? How do…
First Step — Setup The Dask Conda Environment
Please download the files for this article from here http://bit.ly/2okHfgu
Double click create-dask-environment.cmd
Next Step — Setup A Distributed Dask Cluster
A Distributed Dask Cluster consists for a Dask Scheduler and multiple Dask Workers
Start ‘one’ dask scheduler and multiple dask workers as follows…
Double click start-dask-scheduler.cmd
Double click start-dask-worker.cmd 2–3 times to start 2–3 workers
Double click start-dask-web-interface.cmd
Command Line — Dask 2.5.0 documentation
This is the most fundamental way to deploy Dask on multiple machines. In production environments, this process is often…
Finally Lets Start Jupyter
Double Click on start-jupyter.cmd it will launch jupyter in the browser. You will find 5 notebooks, open them one by one and execute them.
This (Below) is where we are heading. And by starting with Dask we have come quite far.
Time to light a cigar…
Where Can I Learn More?
Dask: Scalable analytics in Python
Dask uses existing Python APIs and data structures to make it easy to switch between Numpy, Pandas, Scikit-learn to…
This tutorial was last given at SciPy 2018 in Austin Texas. A video is available online. Dask provides multi-core…
Distributed Pandas on a Cluster with Dask DataFrames
This work is supported by Continuum Analytics theXDATA Program and the Data Driven Discovery Initiative from theMoore…
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